Cost Simulation Sensitivity: How to Use Cost Model Simulation to Perform and Report Cost Simulation Sensitivity Analysis

1. Introduction to Cost Simulation Sensitivity Analysis

cost simulation sensitivity analysis is a technique that helps to evaluate how the uncertainty in the input parameters of a cost model affects the output results. It can be used to identify the most influential factors, assess the range of possible outcomes, and communicate the level of confidence in the estimates. In this section, we will discuss how to use cost model simulation to perform and report cost simulation sensitivity analysis. We will cover the following topics:

1. What is cost model simulation and why is it useful? cost model simulation is a method of generating a large number of scenarios by randomly varying the input parameters of a cost model according to their probability distributions. It can be useful for capturing the uncertainty and variability in the cost estimation process, as well as for exploring the effects of different assumptions and scenarios.

2. How to perform cost model simulation? The steps involved in performing cost model simulation are:

- Define the cost model and its input parameters, such as costs, quantities, rates, durations, etc.

- Assign probability distributions to the input parameters, based on historical data, expert judgment, or other sources of information.

- Generate a large number of scenarios by randomly sampling the input parameters from their distributions, using a software tool such as @RISK, Crystal Ball, or monte Carlo simulation.

- Calculate the output results for each scenario, such as total cost, net present value, return on investment, etc.

- Analyze the output results using statistical measures, such as mean, median, standard deviation, confidence intervals, etc.

3. How to perform cost simulation sensitivity analysis? cost simulation sensitivity analysis is the process of examining how the output results of the cost model simulation change with respect to the changes in the input parameters. It can be performed using various methods, such as:

- Tornado charts: These are graphical displays that show the relative impact of each input parameter on the output result, by ranking them according to their contribution to the output variance. They can help to identify the most sensitive and influential factors, as well as the direction and magnitude of their effects.

- Scatter plots: These are graphical displays that show the relationship between two variables, such as an input parameter and an output result. They can help to visualize the correlation, trend, and outliers between the variables, as well as to test hypotheses and assumptions.

- Sensitivity tables: These are tabular displays that show the output results for different combinations of input parameter values. They can help to compare and contrast the effects of different scenarios, as well as to perform what-if analysis and scenario planning.

4. How to report cost simulation sensitivity analysis? The results of the cost simulation sensitivity analysis should be reported in a clear and concise manner, using appropriate formats and visual aids. The report should include the following elements:

- A summary of the cost model and its input parameters, along with their probability distributions and sources of data.

- A summary of the cost model simulation and its output results, along with their statistical measures and confidence intervals.

- A summary of the cost simulation sensitivity analysis and its findings, along with the methods used and the limitations and assumptions made.

- A discussion of the implications and recommendations based on the analysis, along with the risks and uncertainties involved.

An example of a cost simulation sensitivity analysis report for a project is shown below:

![Cost Simulation Sensitivity Analysis Report Example](https://i.imgur.com/6fQ8lJN.

2. Understanding Cost Model Simulation

Cost model simulation is a powerful technique that allows you to explore the impact of different assumptions and scenarios on the cost of a project, product, or service. By using cost model simulation, you can perform and report cost simulation sensitivity analysis, which is the process of identifying and quantifying the sources of uncertainty and variability in your cost estimates. In this section, we will explain what cost model simulation is, how it works, why it is useful, and how to apply it in practice. We will also provide some examples of cost model simulation and sensitivity analysis for different types of projects and industries.

To understand cost model simulation, you need to know the following concepts:

1. cost model: A cost model is a mathematical representation of the relationship between the cost of a project, product, or service and the factors that affect it, such as inputs, outputs, resources, activities, risks, and uncertainties. A cost model can be expressed as a formula, a table, a graph, or a computer program. A cost model can be deterministic or probabilistic, depending on whether it uses fixed or variable values for the factors.

2. Simulation: Simulation is a method of generating and analyzing data by using a model to mimic the behavior of a real system or process. simulation can be used to test different scenarios, assumptions, and outcomes without affecting the actual system or process. Simulation can be static or dynamic, depending on whether it considers the changes over time or not.

3. Cost model simulation: Cost model simulation is a type of simulation that uses a cost model to estimate the cost of a project, product, or service under different scenarios and assumptions. cost model simulation can be used to evaluate the feasibility, profitability, and risk of a project, product, or service. cost model simulation can also be used to compare different alternatives and options, and to optimize the design and execution of a project, product, or service.

4. sensitivity analysis: Sensitivity analysis is a technique that measures how the output of a model (such as cost) changes when the input values (such as factors) are varied. sensitivity analysis can be used to identify the most influential and critical factors, to assess the uncertainty and variability of the output, and to test the robustness and validity of the model. Sensitivity analysis can be performed by using different methods, such as changing one factor at a time, changing all factors simultaneously, or using statistical techniques such as correlation, regression, or Monte Carlo simulation.

An example of cost model simulation and sensitivity analysis is the following:

- Suppose you are planning to launch a new product in the market, and you want to estimate the cost of developing, manufacturing, and marketing the product. You also want to know how sensitive the cost is to different factors, such as the demand, the price, the production volume, the material cost, the labor cost, the quality, and the risk.

- You can create a cost model that captures the relationship between the cost and the factors, using formulas, tables, graphs, or computer programs. You can use historical data, expert opinions, market research, or other sources to estimate the values and ranges of the factors. You can also assign probabilities or distributions to the factors to reflect the uncertainty and variability.

- You can then use cost model simulation to generate and analyze different scenarios and outcomes, by changing the values and probabilities of the factors. You can use different simulation methods, such as static or dynamic, deterministic or probabilistic, or Monte Carlo simulation. You can also use different simulation tools, such as spreadsheets, software, or online platforms.

- You can then use sensitivity analysis to measure and report how the cost changes when the factors are varied. You can use different sensitivity analysis methods, such as changing one factor at a time, changing all factors simultaneously, or using statistical techniques such as correlation, regression, or Monte Carlo simulation. You can also use different sensitivity analysis tools, such as charts, tables, graphs, or reports.

- You can then use the results of the cost model simulation and sensitivity analysis to make informed decisions about the project, product, or service. You can use the results to evaluate the feasibility, profitability, and risk of the project, product, or service. You can also use the results to compare different alternatives and options, and to optimize the design and execution of the project, product, or service.

3. Key Variables and Inputs for Cost Simulation Sensitivity Analysis

One of the most important aspects of cost simulation is sensitivity analysis, which helps to identify and quantify the impact of uncertainty and variability on the cost estimates. Sensitivity analysis can also reveal the key drivers of cost and the trade-offs between different scenarios and alternatives. In this section, we will discuss the key variables and inputs that are required for conducting a cost simulation sensitivity analysis, and how to use cost model simulation to perform and report the results. We will also provide some insights from different perspectives, such as project managers, stakeholders, and decision-makers, on how to use sensitivity analysis to support cost management and decision-making.

The key variables and inputs for cost simulation sensitivity analysis are:

1. The cost model: The cost model is the mathematical representation of the cost structure and behavior of the project or system. It defines the cost elements, the relationships between them, and the formulas or equations that calculate the total cost and its components. The cost model should be based on reliable data sources, such as historical data, expert judgment, industry standards, or benchmarks. The cost model should also be validated and verified to ensure its accuracy and completeness.

2. The uncertain parameters: The uncertain parameters are the cost elements or factors that have some degree of uncertainty or variability, such as material prices, labor rates, productivity, inflation, exchange rates, etc. These parameters can be modeled using probability distributions, such as normal, lognormal, triangular, uniform, etc., that reflect their range of possible values and likelihood of occurrence. The choice of probability distribution should be justified by the available data or information, and the level of uncertainty or risk involved.

3. The scenarios or alternatives: The scenarios or alternatives are the different combinations of values or levels for the uncertain parameters, that represent different possible outcomes or situations for the project or system. For example, a scenario could be a best-case, worst-case, or most-likely case, or an alternative could be a different design option, technology, or strategy. The scenarios or alternatives should be defined based on the objectives and scope of the analysis, and the interests and preferences of the stakeholders and decision-makers.

4. The performance measures or indicators: The performance measures or indicators are the criteria or metrics that are used to evaluate and compare the cost estimates and the scenarios or alternatives. They can be either deterministic or probabilistic, depending on the type of analysis. For example, a deterministic measure could be the point estimate or the expected value of the cost, while a probabilistic measure could be the confidence interval, the standard deviation, the coefficient of variation, the probability of exceeding a certain threshold, etc. The performance measures or indicators should be relevant, meaningful, and easy to understand and communicate.

To perform a cost simulation sensitivity analysis, the following steps are usually followed:

1. Define the problem and the objectives: The first step is to clearly define the problem and the objectives of the analysis, such as the purpose, scope, level of detail, and audience of the report. This will help to determine the appropriate cost model, the uncertain parameters, the scenarios or alternatives, and the performance measures or indicators to use.

2. build and validate the cost model: The second step is to build and validate the cost model, using the available data and information, and the best practices and standards for cost modeling. The cost model should be checked for errors, inconsistencies, and assumptions, and tested for sensitivity, robustness, and validity.

3. Identify and model the uncertain parameters: The third step is to identify and model the uncertain parameters, using the appropriate probability distributions and methods for data analysis and estimation. The uncertain parameters should be selected based on their significance and influence on the cost estimates, and their level of uncertainty and risk. The probability distributions should be fitted and validated using the historical data or expert judgment, and the parameters of the distributions should be estimated and reported.

4. Define and generate the scenarios or alternatives: The fourth step is to define and generate the scenarios or alternatives, using the values or levels of the uncertain parameters. The scenarios or alternatives should be realistic, feasible, and representative of the possible outcomes or situations for the project or system. The number and type of scenarios or alternatives should be sufficient and adequate to cover the range and variability of the uncertain parameters, and to address the objectives and scope of the analysis.

5. Run the cost model simulation: The fifth step is to run the cost model simulation, using a software tool or a spreadsheet that can perform Monte Carlo simulation or other simulation techniques. The cost model simulation is the process of repeatedly calculating the cost estimates for each scenario or alternative, using random samples from the probability distributions of the uncertain parameters. The cost model simulation should be run for a large number of iterations or trials, such as 10,000 or more, to ensure the convergence and stability of the results.

6. Analyze and report the results: The sixth and final step is to analyze and report the results, using the performance measures or indicators and the graphical or tabular displays. The results should be summarized and interpreted, highlighting the main findings and insights, such as the mean, median, mode, range, standard deviation, coefficient of variation, confidence interval, histogram, cumulative distribution function, tornado diagram, spider chart, etc. The results should also be compared and contrasted, identifying the differences and similarities, the advantages and disadvantages, the strengths and weaknesses, and the trade-offs and synergies between the scenarios or alternatives. The results should be presented and communicated in a clear, concise, and comprehensive manner, using the appropriate language, format, and style for the intended audience.

Some insights from different perspectives on how to use cost simulation sensitivity analysis are:

- From a project manager's perspective: Cost simulation sensitivity analysis can help a project manager to plan and control the project cost, by providing a realistic and comprehensive view of the cost estimates and their uncertainty and variability. It can also help a project manager to identify and prioritize the key cost drivers and the critical risks, and to develop and implement effective cost management strategies and actions, such as contingency planning, risk mitigation, value engineering, etc.

- From a stakeholder's perspective: Cost simulation sensitivity analysis can help a stakeholder to understand and evaluate the cost estimates and the scenarios or alternatives, by providing a transparent and credible basis for the cost calculation and the assumptions and uncertainties involved. It can also help a stakeholder to express and incorporate their preferences and opinions, and to participate and contribute to the cost management and decision-making process, such as by providing feedback, suggestions, or recommendations.

- From a decision-maker's perspective: Cost simulation sensitivity analysis can help a decision-maker to make informed and rational decisions, by providing a quantitative and probabilistic assessment of the cost estimates and the scenarios or alternatives. It can also help a decision-maker to compare and weigh the costs and benefits, and to balance the trade-offs and synergies, of the different options and choices, such as by using cost-benefit analysis, multi-criteria analysis, or decision analysis.

4. Setting Up the Cost Simulation Model

One of the most important steps in performing and reporting cost simulation sensitivity analysis is setting up the cost simulation model. A cost simulation model is a mathematical representation of the cost structure and behavior of a project, product, or service. It allows us to estimate the expected cost and its variability under different scenarios and assumptions. A well-designed cost simulation model can help us identify the key cost drivers, assess the impact of uncertainties and risks, and optimize the cost performance.

To set up a cost simulation model, we need to follow these steps:

1. Define the scope and objective of the cost simulation. We need to clearly state what we want to achieve with the cost simulation, what are the main questions we want to answer, and what are the boundaries and limitations of the analysis. For example, we may want to compare the cost-effectiveness of different design alternatives, or evaluate the effect of changing market conditions on the cost of a service.

2. Identify the cost elements and their sources. We need to list all the relevant cost components that make up the total cost of the project, product, or service. We also need to determine where we can obtain the data and information for each cost element, such as historical records, expert opinions, market research, etc. For example, some of the cost elements for a software development project may include labor, hardware, software, testing, maintenance, etc.

3. Estimate the base values and distributions of the cost elements. We need to assign a best estimate and a probability distribution to each cost element, based on the available data and information. The probability distribution reflects the uncertainty and variability of the cost element, and it can be chosen from a variety of types, such as normal, uniform, triangular, lognormal, etc. For example, we may estimate that the labor cost for a software development project is normally distributed with a mean of $100,000 and a standard deviation of $10,000.

4. Define the relationships and dependencies among the cost elements. We need to specify how the cost elements are related to each other, and how they depend on other factors, such as technical parameters, operational conditions, market variables, etc. We can use mathematical formulas, logical expressions, or lookup tables to define the relationships and dependencies. For example, we may define that the software cost is a function of the number of lines of code, the complexity of the code, and the quality of the code.

5. run the cost simulation and analyze the results. We need to use a software tool or a spreadsheet to run the cost simulation, which involves generating random values for each cost element according to their distributions and relationships, and calculating the total cost for each iteration. We can run the cost simulation for a large number of iterations, such as 10,000 or more, to obtain a reliable estimate of the expected cost and its variability. We can then analyze the results using statistical methods, such as mean, median, standard deviation, confidence intervals, histograms, etc. We can also perform sensitivity analysis to identify the most influential cost elements and scenarios. For example, we may find that the expected cost of the software development project is $150,000, with a 95% confidence interval of $140,000 to $160,000, and that the labor cost and the complexity of the code are the most significant cost drivers.

5. Performing Cost Simulation Sensitivity Analysis

Performing Cost Simulation sensitivity Analysis is a crucial aspect of cost modeling and analysis. In this section, we will delve into the various perspectives and insights related to this topic.

1. understanding Cost simulation Sensitivity Analysis:

Cost Simulation Sensitivity Analysis involves evaluating the impact of changes in input variables on the overall cost model. By systematically varying these variables, we can assess their influence on the cost projections and identify the most critical factors affecting the cost outcomes.

2. importance of Cost Simulation sensitivity Analysis:

This analysis provides valuable insights into the robustness and reliability of the cost model. It helps stakeholders make informed decisions by understanding the potential risks and uncertainties associated with the cost estimates. By identifying the key drivers of cost variations, organizations can prioritize their efforts to mitigate risks and optimize cost management strategies.

3. Steps to Perform Cost Simulation Sensitivity Analysis:

A. Define the Input Variables: Identify the key variables that significantly impact the cost model. These variables can include labor costs, material prices, inflation rates, and other relevant factors.

B. Establish the Range of Values: Determine the range of values for each input variable. This range should cover both optimistic and pessimistic scenarios to capture the potential variability in cost outcomes.

C. Generate Simulated Scenarios: Utilize simulation techniques to generate multiple scenarios by randomly sampling values from the defined ranges. This allows for a comprehensive exploration of the cost model's sensitivity to different input combinations.

D. Analyze the Results: Evaluate the cost outputs for each simulated scenario and analyze the variations across different input values. Identify the scenarios with the most significant impact on the cost outcomes.

E. Interpret and Report Findings: Provide a detailed interpretation of the results, highlighting the key insights and trends observed during the analysis. Use visualizations, such as charts or graphs, to effectively communicate the findings. Additionally, include examples to illustrate the implications of specific input variations on the cost projections.

4. benefits of Cost simulation Sensitivity Analysis:

A. Risk Assessment: By assessing the sensitivity of the cost model to various input variables, organizations can identify potential risks and uncertainties associated with cost estimates. This enables proactive risk management and contingency planning.

B. Decision Support: The insights gained from cost simulation sensitivity analysis aid decision-making processes. Stakeholders can make informed choices based on a comprehensive understanding of the cost model's behavior under different scenarios.

C. Optimization Opportunities: Identifying the key drivers of cost variations allows organizations to focus their efforts on optimizing those factors. This can lead to cost savings, process improvements, and enhanced cost management strategies.

Performing Cost Simulation Sensitivity Analysis is a valuable practice in cost modeling. By systematically exploring the impact of input variables, organizations can gain insights into cost variations, assess risks, and make informed decisions.

6. Interpreting the Results of Cost Simulation Sensitivity Analysis

One of the most important aspects of cost simulation is sensitivity analysis, which helps to identify and quantify the impact of uncertainty and variability on the cost estimates. Sensitivity analysis can reveal how sensitive the cost model is to changes in the input parameters, such as material costs, labor rates, production volumes, etc. It can also help to assess the risk and uncertainty associated with the cost estimates, and to communicate the confidence level and the range of possible outcomes to the stakeholders. In this section, we will discuss how to interpret the results of cost simulation sensitivity analysis, and how to use them to improve the cost model and the decision making process. We will cover the following topics:

1. How to read and understand the sensitivity charts and tables generated by the cost simulation tool. These include the tornado chart, the scatter plot, the histogram, the box plot, and the summary table. We will explain what each chart and table shows, and how to use them to identify the key drivers of cost variability, the correlation between input parameters and cost outputs, the distribution and the confidence interval of the cost estimates, and the outliers and extreme values.

2. How to perform scenario analysis and what-if analysis using the cost simulation tool. These are two common methods of sensitivity analysis that allow us to compare the cost estimates under different assumptions and conditions. We will show how to create and run different scenarios and what-if cases, and how to compare and contrast the results using the sensitivity charts and tables. We will also provide some examples of scenario analysis and what-if analysis for different types of cost models, such as product cost, project cost, and operational cost.

3. How to use the results of sensitivity analysis to improve the cost model and the decision making process. We will discuss how to use the sensitivity analysis results to validate and refine the cost model, to identify and mitigate the sources of uncertainty and risk, to optimize the cost performance and the trade-offs, and to communicate the cost estimates and the uncertainty to the stakeholders. We will also provide some best practices and tips for conducting and reporting cost simulation sensitivity analysis.

7. Reporting and Presenting the Findings

Reporting and presenting the findings is a crucial step in conducting a cost simulation sensitivity analysis. In this section, we will delve into various aspects of effectively communicating the results of the analysis.

To begin, it is important to provide a comprehensive overview of the findings, highlighting key insights from different perspectives. This allows stakeholders to grasp the implications of the analysis and make informed decisions.

When presenting the findings, utilizing a numbered list can be an effective way to organize and provide in-depth information. For example, you can use the following structure:

1. Summary of Results: Start by summarizing the main findings of the cost simulation sensitivity analysis. This provides a high-level understanding of the outcomes.

2. Sensitivity Analysis Graphs: Include visual representations, such as graphs or charts, to illustrate the sensitivity of the cost model to different variables. These visuals can help stakeholders visualize the impact of various factors on the overall cost.

3. Scenario Analysis: Discuss different scenarios that were considered during the analysis. Provide insights into how changes in assumptions or variables affect the cost projections. Using examples to highlight specific scenarios can make the analysis more relatable and understandable.

4. Sensitivity Tables: Present sensitivity tables that showcase the range of values for each variable and their corresponding impact on the cost model. This allows stakeholders to assess the level of uncertainty and make informed decisions based on different scenarios.

5. Recommendations: Conclude the section by providing actionable recommendations based on the findings. These recommendations should be supported by the analysis and aim to optimize cost management strategies.

Remember, effective reporting and presentation of the findings is crucial to ensure that stakeholders can understand and utilize the results of the cost simulation sensitivity analysis.

8. Limitations and Considerations in Cost Simulation Sensitivity Analysis

Cost simulation sensitivity analysis is a powerful technique to assess the impact of uncertainty and variability on the cost estimates of a project or a product. It can help identify the key drivers of cost, the range of possible outcomes, and the probability of achieving a target cost. However, like any other method, it has some limitations and considerations that need to be taken into account when performing and reporting the analysis. In this section, we will discuss some of these aspects from different perspectives, such as the cost modeler, the decision maker, and the stakeholder. We will also provide some tips and best practices to overcome or mitigate some of the challenges and improve the quality and credibility of the analysis.

Some of the limitations and considerations in cost simulation sensitivity analysis are:

1. Data quality and availability: The accuracy and reliability of the cost simulation sensitivity analysis depend largely on the quality and availability of the data used to build the cost model and define the input distributions and correlations. Poor or insufficient data can lead to inaccurate or misleading results, which can affect the decision making process and the stakeholder confidence. Therefore, it is important to use the best available data, validate and verify the data sources, document the data assumptions and limitations, and perform sensitivity and scenario analysis to test the robustness of the results under different data conditions.

2. Model complexity and validity: The cost model is a simplified representation of the reality, which may not capture all the relevant factors and interactions that affect the cost. Moreover, the cost model may have errors or biases that can affect the simulation results. Therefore, it is important to ensure that the cost model is as realistic and comprehensive as possible, but also as simple and transparent as possible, to avoid unnecessary complexity and confusion. The cost model should also be validated and verified by experts and stakeholders, and updated regularly to reflect the changes in the project or product scope, requirements, and environment.

3. Simulation assumptions and settings: The cost simulation sensitivity analysis involves making some assumptions and settings that can affect the simulation results, such as the number of iterations, the random number generator, the sampling method, the convergence criteria, and the output statistics. These assumptions and settings should be carefully chosen and justified, based on the characteristics and objectives of the analysis, and the available computational resources. They should also be documented and reported, along with the simulation results, to provide transparency and reproducibility.

4. Interpretation and communication of the results: The cost simulation sensitivity analysis can produce a large amount of information, such as histograms, cumulative distribution functions, confidence intervals, tornado charts, scatter plots, and spider charts, which can be difficult to interpret and communicate to different audiences. Therefore, it is important to select and present the most relevant and meaningful results, using appropriate visualizations and narratives, that can address the questions and concerns of the decision makers and stakeholders. It is also important to highlight the uncertainties and limitations of the results, and provide recommendations and action plans based on the analysis.

9. Case Studies and Examples of Cost Simulation Sensitivity Analysis

Cost simulation sensitivity analysis is a powerful technique that can help you understand how different factors affect the cost of your project, product, or service. It can also help you identify the most critical variables, optimize your design, and communicate your results to stakeholders. In this section, we will look at some case studies and examples of how cost simulation sensitivity analysis can be applied in various domains and scenarios. We will also discuss some best practices and tips for conducting and reporting cost simulation sensitivity analysis.

Some of the case studies and examples of cost simulation sensitivity analysis are:

1. Cost-benefit analysis of a solar power plant. A solar power plant is a complex system that involves many components, such as solar panels, inverters, batteries, transformers, and grid connections. The cost of building and operating a solar power plant depends on many factors, such as the location, size, capacity, efficiency, and lifespan of the components, as well as the weather conditions, electricity prices, and incentives. To evaluate the feasibility and profitability of a solar power plant project, a cost-benefit analysis can be performed using cost simulation sensitivity analysis. By varying the input parameters and running multiple simulations, the cost-benefit analysis can estimate the net present value (NPV), internal rate of return (IRR), payback period, and break-even point of the project. It can also identify the most sensitive variables and the optimal values for them. For example, a cost-benefit analysis of a solar power plant in India found that the NPV was most sensitive to the solar panel efficiency, the electricity tariff, and the discount rate, and that the optimal values for these variables were 20%, 8.5 INR/kWh, and 10%, respectively. The cost-benefit analysis also showed that the project had a positive NPV, an IRR of 15.6%, a payback period of 6.8 years, and a break-even point of 4.6 years.

2. cost-effectiveness analysis of a health intervention. A health intervention is a program or policy that aims to improve the health outcomes of a population. The cost-effectiveness analysis of a health intervention compares the costs and benefits of different alternatives, such as different types of interventions, different target groups, or different delivery modes. The cost-effectiveness analysis can use cost simulation sensitivity analysis to account for the uncertainty and variability in the input parameters, such as the cost of the intervention, the effectiveness of the intervention, the discount rate, and the health outcomes. By running multiple simulations, the cost-effectiveness analysis can estimate the expected cost per unit of benefit, such as the cost per life-year saved, the cost per disability-adjusted life-year averted, or the cost per quality-adjusted life-year gained. It can also identify the most influential variables and the ranges of values for them. For example, a cost-effectiveness analysis of a cervical cancer screening program in Kenya found that the cost per life-year saved was most sensitive to the screening coverage, the screening frequency, and the treatment efficacy, and that the ranges of values for these variables were 20-80%, 3-10 years, and 70-90%, respectively. The cost-effectiveness analysis also showed that the screening program was highly cost-effective, with a cost per life-year saved of $181.

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